Support Conditional Hyperparameter in AMT

**Describe the feature you'd like**

Hyperparameter tuning frameworks like [optuna](https://optuna.org/) and google's [vertex AI](https://cloud.google.com/vertex-ai/docs/training/hyperparameter-tuning-overview#conditional_hyperparameters) all support some form of conditional hyperparameter. Could you consider supporting this feature in some capacity?

**How would this feature be used? Please describe.**

For instance, in training a cnn, we can define hyperparameters conditional on their parent hyperparameter matching some condition:

```
search_space = {}
if optimizer == 'adam':
search_space['adam_beta_1'] = range(...)
search_space['adam_beta_2'] = range(...)
elif optimizer == 'sgd':
...
```

**Describe alternatives you've considered**

Right now there are a few ways to work around this:

* Using optuna with sagemaker
* Enumerate all possible hyperparameters and handle appropriately in the training entry point (i.e., ignore the ones not needed)

It would be very helpful if this feature is supported by default. Thanks.

Non sono indicati file, test o punti di ingresso. Inizia esaminando il flusso esistente di ottimizzazione degli iperparametri di AMT e la documentazione collegata di Optuna e Vertex AI sugli iperparametri condizionali, quindi definisci come rappresentare e convalidare lo spazio di ricerca condizionale dell’esempio CNN.

Scritto dal modello di indicizzazione a partire dal testo della issue.